OpenRaiser/Intern-Atlas huggingface dataset:
Image2Net: Datasets, Benchmark and Hybrid Framework to Convert Analog Circuit Diagrams into Netlists
Venue: arXiv cs.AR 2025
Year: 2025

Large Language Model (LLM) exhibits great potential in designing of analog integrated circuits (IC) because of its excellence in abstraction and generalization for knowledge. However, further development of LLM-based analog ICs heavily relies on textual description of analog ICs, while existing analog ICs are mostly illustrated in image-based circuit diagrams rather than text-based netlists. Converting circuit diagrams to netlists help LLMs to enrich the knowledge of analog IC. Nevertheless, previously proposed conversion frameworks face challenges in further application because of limited support of image styles and circuit elements. Up to now, it still remains a challenging task to effectively convert complex circuit diagrams into netlists. To this end, this paper constructs and opensources a new dataset with rich styles of circuit diagrams as well as balanced distribution of simple and complex analog ICs. And a hybrid framework, named Image2Net, is proposed for practical conversion from circuit diagrams to netlists. The netlist edit distance (NED) is also introduced to precisely assess the difference between the converted netlists and ground truth. Based on our benchmark, Image2Net achieves 80.77% successful rate, which is 34.62%-45.19% higher than previous works. Specifically, the proposed work shows 0.116 averaged NED, which is 62.1%-69.6% lower than state-of-the-arts. Our datasets and benchmark are available at https://github.com/LAD021/ci2n_datasets.
Accelerating Transistor-Level Simulation of Integrated Circuits via Equivalence of RC Long-Chain Structures
Venue: arXiv cs.AR 2025
Year: 2025

Transistor-level simulation plays a vital role in validating the physical correctness of integrated circuits. However, such simulations are computationally expensive. This paper proposes three novel reduction methods specifically tailored to RC long-chain structures with different scales of time constant. Such structures account for an average of 6.34\% (up to 12\%) of the total nodes in the benchmark circuits. Experimental results demonstrate that our methods yields an average performance improvement of 8.8\% (up to 22\%) on simulating benchmark circuits which include a variety of functional modules such as ALUs, adders, multipliers, SEC/DED checkers, and interrupt controllers, with only 0.7\% relative error.
Through Silicon Via Aware Design Planning for Thermally Efficient 3-D Integrated Circuits
Venue: arXiv cs.AR 2025
Year: 2025

3-D integrated circuits (3-D ICs) offer performance advantages due to their increased bandwidth and reduced wire-length enabled by through-silicon-via structures (TSVs). Traditionally TSVs have been considered to improve the thermal conductivity in the vertical direction. However, the lateral thermal blockage effect becomes increasingly important for TSV via farms (a cluster of TSV vias used for signal bus connections between layers) because the TSV size and pitch continue to scale in μm range and the metal to insulator ratio becomes smaller. Consequently, dense TSV farms can create lateral thermal blockages in thinned silicon substrate and exacerbate the local hotspots. In this paper, we propose a thermal-aware via farm placement technique for 3-D ICs to minimize lateral heat blockages caused by dense signal bus TSV structures.
FedChip: Federated LLM for Artificial Intelligence Accelerator Chip Design
Venue: arXiv cs.AR 2025
Year: 2025

AI hardware design is advancing rapidly, driven by the promise of design automation to make chip development faster, more efficient, and more accessible to a wide range of users. Amongst automation tools, Large Language Models (LLMs) offer a promising solution by automating and streamlining parts of the design process. However, their potential is hindered by data privacy concerns and the lack of domain-specific training. To address this, we introduce FedChip, a Federated fine-tuning approach that enables multiple Chip design parties to collaboratively enhance a shared LLM dedicated for automated hardware design generation while protecting proprietary data. FedChip enables parties to train the model on proprietary local data and improve the shared LLM's performance. To exemplify FedChip's deployment, we create and release APTPU-Gen, a dataset of 30k design variations spanning various performance metric values such as power, performance, and area (PPA). To encourage the LLM to generate designs that achieve a balance across multiple quality metrics, we propose a new design evaluation metric, Chip@k, which statistically evaluates the quality of generated designs against predefined acceptance criteria. Experimental results show that FedChip improves design quality by more than 77% over high-end LLMs while maintaining data privacy
A novel geometric predictive algorithm for assessing Compressive Elastic Modulus in MEX additive processes, based on part nonlinearities and layers stiffness,validated with PETG and PLA materials
Venue: arXiv cs.CE 2025
Year: 2025

The paper presents an innovative methodology based on the use of a new predictive algorithm created by the researchers capable of obtaining the elastic modulus of a plastic material manufactured with MEX and its mechanical behaviour in the elastic zone under compressive loads. The predictive algorithm only needs as input the compressive elastic modulus of the isotropic plastic material filament and the manufacturing parameters of the MEX process. The smart developed algorithm calculates the stiffness of each layer considering the number of holes in the projected area. The innovative predictive algorithm has been experimentally and numerically validated using PETG Polyethylene Terephthalate Glycol material and PLA Polylactic Acid on test specimens and on a case study of variable topology. The predictive algorithm is valid for each print pattern and manufacturing direction. The new algorithm improves the existing state of the art significantly since this algorithm extends its utility to most plastic polymer materials suitable for MEX 3D printing, provided that the mechanical and elastic properties of the filament are known. Its versatility extends to complex component geometries subjected to uniaxial compression loads, eliminating the need for mechanical analysis software or expensive experimental validations.
A Hardware-oriented Approach for Efficient Active Inference Computation and Deployment
Venue: arXiv cs.AI 2025
Year: 2025

Active Inference (AIF) offers a robust framework for decision-making, yet its computational and memory demands pose challenges for deployment, especially in resource-constrained environments. This work presents a methodology that facilitates AIF's deployment by integrating pymdp's flexibility and efficiency with a unified, sparse, computational graph tailored for hardware-efficient execution. Our approach reduces latency by over 2x and memory by up to 35%, advancing the deployment of efficient AIF agents for real-time and embedded applications.
Search-Time Data Contamination
Venue: arXiv cs.AI 2025
Year: 2025

Data contamination refers to the leakage of evaluation data into model training data, resulting in overfitting to supposedly held-out test sets and compromising test validity. We identify an analogous issue, search-time contamination (STC), in evaluating search-based LLM agents which use tools to gather information from online sources when answering user queries. STC occurs when the retrieval step surfaces a source containing the test question (or a near-duplicate) alongside its answer, enabling agents to copy rather than genuinely infer or reason, undermining benchmark integrity. We find that HuggingFace, an online platform hosting evaluation datasets, appears among retrieved sources in search based agent logs. Consequently, agents often explicitly acknowledge discovering question answer pairs from HuggingFace within their reasoning chains. On three commonly used capability benchmarks: Humanity's Last Exam (HLE), SimpleQA, and GPQA, we demonstrate that for approximately 3% of questions, search-based agents directly find the datasets with ground truth labels on HuggingFace. When millions of evaluation queries target the same benchmark, even small, repeated leaks can accelerate the benchmark's obsolescence, shortening its intended lifecycle. After HuggingFace is blocked, we observe a drop in accuracy on the contaminated subset of approximately 15%. We further show through ablation experiments that publicly accessible evaluation datasets on HuggingFace may not be the sole source of STC. To this end, we conclude by proposing best practices for benchmark design and result reporting to address this novel form of leakage and ensure trustworthy evaluation of search-based LLM agents. To facilitate the auditing of evaluation results, we also publicly release the complete logs from our experiments.
When AI Writes Back: Ethical Considerations by Physicians on AI-Drafted Patient Message Replies
Venue: arXiv cs.CY 2025
Year: 2025

The increasing burden of responding to large volumes of patient messages has become a key factor contributing to physician burnout. Generative AI (GenAI) shows great promise to alleviate this burden by automatically drafting patient message replies. The ethical implications of this use have however not been fully explored. To address this knowledge gap, we conducted a semi-structured interview study with 21 physicians who participated in a GenAI pilot program. We found that notable ethical considerations expressed by the physician participants included human oversight as ethical safeguard, transparency and patient consent of AI use, patient misunderstanding of AI's role, and patient privacy and data security as prerequisites. Additionally, our findings suggest that the physicians believe the ethical responsibility of using GenAI in this context primarily lies with users, not with the technology. These findings may provide useful insights into guiding the future implementation of GenAI in clinical practice.
A Recurrent Neural Network based Clustering Method for Binary Data Sets in Education
Venue: arXiv cs.LG 2025
Year: 2025

This paper studies an application of a recurrent neural network to clustering method for the S-P chart: a binary data set used widely in education. As the number of students increases, the S-P chart becomes hard to handle. In order to classify the large chart into smaller charts, we present a simple clustering method based on the network dynamics. In the method, the network has multiple fixed points and basins of attraction give clusters corresponding to small S-P charts. In order to evaluate the clustering performance, we present an important feature quantity: average caution index that characterizes singularity of students answer oatterns. Performing fundamental experiments, effectiveness of the method is confirmed.
Quantifying Loss Aversion in Cyber Adversaries via LLM Analysis
Venue: arXiv cs.CR 2025
Year: 2025

Understanding and quantifying human cognitive biases from empirical data has long posed a formidable challenge, particularly in cybersecurity, where defending against unknown adversaries is paramount. Traditional cyber defense strategies have largely focused on fortification, while some approaches attempt to anticipate attacker strategies by mapping them to cognitive vulnerabilities, yet they fall short in dynamically interpreting attacks in progress. In recognition of this gap, IARPA's ReSCIND program seeks to infer, defend against, and even exploit attacker cognitive traits. In this paper, we present a novel methodology that leverages large language models (LLMs) to extract quantifiable insights into the cognitive bias of loss aversion from hacker behavior. Our data are collected from an experiment in which hackers were recruited to attack a controlled demonstration network. We process the hacker generated notes using LLMs using it to segment the various actions and correlate the actions to predefined persistence mechanisms used by hackers. By correlating the implementation of these mechanisms with various operational triggers, our analysis provides new insights into how loss aversion manifests in hacker decision-making. The results demonstrate that LLMs can effectively dissect and interpret nuanced behavioral patterns, thereby offering a transformative approach to enhancing cyber defense strategies through real-time, behavior-based analysis.
Data driven feedback linearization of nonlinear control systems via Lie derivatives and stacked regression approach
Venue: arXiv cs.LG 2025
Year: 2025

Discovering the governing equations of a physical system and designing an effective feedback controller remains one of the most challenging and intensive areas of ongoing research. This task demands a deep understanding of the system behavior, including the nonlinear factors that influence its dynamics. In this article, we propose a novel methodology for identifying a feedback linearized physical system based on known prior dynamic behavior. Initially, the system is identified using a sparse regression algorithm, subsequently a feedback controller is designed for the discovered system by applying Lie derivatives to the dictionary of output functions to derive an augmented constraint which guarantees that no internal dynamics are observed. Unlike the prior related works, the novel aspect of this article combines the approach of stacked regression algorithm and relative degree conditions to discover and feedback linearize the true governing equations of a physical model.
Exploration of Deep Learning Based Recognition for Urdu Text
Venue: arXiv cs.CV 2025
Year: 2025

Urdu is a cursive script language and has similarities with Arabic and many other South Asian languages. Urdu is difficult to classify due to its complex geometrical and morphological structure. Character classification can be processed further if segmentation technique is efficient, but due to context sensitivity in Urdu, segmentation-based recognition often results with high error rate. Our proposed approach for Urdu optical character recognition system is a component-based classification relying on automatic feature learning technique called convolutional neural network. CNN is trained and tested on Urdu text dataset, which is generated through permutation process of three characters and further proceeds to discarding unnecessary images by applying connected component technique in order to obtain ligature only. Hierarchical neural network is implemented with two levels to deal with three degrees of character permutations and component classification Our model successfully achieved 0.99% for component classification.
Datarus-R1: An Adaptive Multi-Step Reasoning LLM for Automated Data Analysis
Venue: arXiv cs.CL 2025
Year: 2025

We present Datarus-R1-14B, a 14 B-parameter open-weights language model fine-tuned from Qwen 2.5-14B-Instruct to act as a virtual data analyst and graduate-level problem solver. Datarus is trained not on isolated question-answer pairs but on full analytical trajectories including reasoning steps, code execution, error traces, self-corrections, and final conclusions, all captured in a ReAct-style notebook format spanning finance, medicine, numerical analysis, and other quantitative domains. Our training pipeline combines (i) a trajectory-centric synthetic data generator that yielded 144 000 tagged notebook episodes, (ii) a dual-reward framework blending a lightweight tag-based structural signal with a Hierarchical Reward Model (HRM) that scores both single-step soundness and end-to-end coherence, and (iii) a memory-optimized implementation of Group Relative Policy Optimization (GRPO) featuring KV-cache reuse, sequential generation, and reference-model sharding. A cosine curriculum smoothly shifts emphasis from structural fidelity to semantic depth, reducing the format collapse and verbosity that often plague RL-aligned LLMs. A central design choice in Datarus is it dual reasoning interface. In agentic mode the model produces ReAct-tagged steps that invoke Python tools to execute real code; in reflection mode it outputs compact Chain-of-Thought (CoT) traces delimited by <think> and <answer> tags. On demanding postgraduate-level problems, Datarus exhibits an "AHA-moment" pattern: it sketches hypotheses, revises them once or twice, and converges avoiding the circular, token-inflating loops common to contemporary systems. Across standard public benchmarks Datarus surpasses similar size models and even reaches the level of larger reasoning models such as QwQ-32B achieving up to 30% higher accuracy on AIME 2024/2025 and LiveCodeBench while emitting 18-49% fewer tokens per solution.
SPANER: Shared Prompt Aligner for Multimodal Semantic Representation
Venue: arXiv cs.AI 2025
Year: 2025

Recent advances in multimodal Parameter-Efficient Fine-Tuning (PEFT) have significantly improved performance on downstream tasks such as few-shot retrieval. However, most existing approaches focus on task-specific gains while neglecting the structure of the multimodal embedding space. As a result, modality-specific representations often remain isolated, limiting cross-modal generalisation. In this work, we introduce Shared Prompt AligNER (SPANER), a modality-agnostic PEFT framework designed to embed inputs from diverse modalities into a unified semantic space. At its core, SPANER employs a shared prompt mechanism that acts as a conceptual anchor, enabling semantically related instances to converge spatially regardless of modality. This shared prompt design is inherently extensible, supporting the seamless integration of additional modalities, such as audio, without altering the core architecture. Through comprehensive experiments across vision-language and audio-visual benchmarks, SPANER demonstrates competitive few-shot retrieval performance while preserving high semantic coherence in the learned embedding space. Our results highlight the importance of aligning embedding structures, rather than merely tuning adapter weights, for scalable multimodal learning.
Data Work in Memory Institutions: Why and How Information Professionals Use Wikidata
Venue: arXiv cs.HC 2025
Year: 2025

Wikidata, an open structured database and a sibling project to Wikipedia, has recently become an important platform for information professionals to share structured metadata from their memory institutions, organizations that maintain public knowledge and cultural heritage materials. While studies have investigated why and how peer producers contribute to Wikidata, the institutional motivations and practices of these organizations are less understood. Given Wikidata's potential role in linking and supporting knowledge infrastructures and open data systems, we examined why and how information professionals in memory institutions use Wikidata as part of their organizational workflow. Through interviews with 15 participants, we identified the three archetypal roles of Wikidata users within memory institutions, providers, acquirers, and mutualists, and the different types of contributions that these institutions bring to Wikidata. We then explored potential collaboration opportunities between memory institutions and other volunteers in Wikidata, discussed the value of the data work conducted by these professionals, and examined how and why they track their contributions. Our work contributes to the wider discussions around collaboration and data work in CSCW by (1) studying the motivations and practices of information professionals, their differences from those doing volunteer work, and opportunities for the Wikidata community to promote more collaborative efforts within memory institutions and with other volunteers and (2) drawing attention to the important data work done by memory institutions on Wikidata and pointing out opportunities to support the contributions of information professionals.
A Comparative Study of Delta Parquet, Iceberg, and Hudi for Automotive Data Engineering Use Cases
Venue: arXiv cs.SE 2025
Year: 2025

The automotive industry generates vast amounts of data from sensors, telemetry, diagnostics, and real-time operations. Efficient data engineering is critical to handle challenges of latency, scalability, and consistency. Modern data lakehouse formats Delta Parquet, Apache Iceberg, and Apache Hudi offer features such as ACID transactions, schema enforcement, and real-time ingestion, combining the strengths of data lakes and warehouses to support complex use cases. This study presents a comparative analysis of Delta Parquet, Iceberg, and Hudi using real-world time-series automotive telemetry data with fields such as vehicle ID, timestamp, location, and event metrics. The evaluation considers modeling strategies, partitioning, CDC support, query performance, scalability, data consistency, and ecosystem maturity. Key findings show Delta Parquet provides strong ML readiness and governance, Iceberg delivers high performance for batch analytics and cloud-native workloads, while Hudi is optimized for real-time ingestion and incremental processing. Each format exhibits tradeoffs in query efficiency, time-travel, and update semantics. The study offers insights for selecting or combining formats to support fleet management, predictive maintenance, and route optimization. Using structured datasets and realistic queries, the results provide practical guidance for scaling data pipelines and integrating machine learning models in automotive applications.
Decentralized Contextual Bandits with Network Adaptivity
Venue: arXiv cs.LG 2025
Year: 2025

We consider contextual linear bandits over networks, a class of sequential decision-making problems where learning occurs simultaneously across multiple locations and the reward distributions share structural similarities while also exhibiting local differences. While classical contextual bandits assume either fully centralized data or entirely isolated learners, much remains unexplored in networked environments when information is partially shared. In this paper, we address this gap by developing two network-aware Upper Confidence Bound (UCB) algorithms, NetLinUCB and Net-SGD-UCB, which enable adaptive information sharing guided by dynamically updated network weights. Our approach decompose learning into global and local components and as a result allow agents to benefit from shared structure without full synchronization. Both algorithms incur lighter communication costs compared to a fully centralized setting as agents only share computed summaries regarding the homogeneous features. We establish regret bounds showing that our methods reduce the learning complexity associated with the shared structure from $O(N)$ to sublinear $O(\sqrt{N})$, where $N$ is the size of the network. The two algorithms reveal complementary strengths: NetLinUCB excels in low-noise regimes with fine-grained heterogeneity, while Net-SGD-UCB is robust to high-dimensional, high-variance contexts. We further demonstrate the effectiveness of our methods across simulated pricing environments compared to standard benchmarks.